Dennis Prangle
Cited by
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Constructing summary statistics for approximate Bayesian computation: semi‐automatic approximate Bayesian computation
P Fearnhead, D Prangle
Journal of the Royal Statistical Society: Series B (Statistical Methodology …, 2012
A comparative review of dimension reduction methods in approximate Bayesian computation
MGB Blum, MA Nunes, D Prangle, SA Sisson
Arxiv preprint arXiv:1202.3819, 2012
Adapting the ABC distance function
D Prangle
Bayesian Analysis 12 (1), 289-309, 2017
A tutorial introduction to Bayesian inference for stochastic epidemic models using Approximate Bayesian Computation
T Kypraios, P Neal, D Prangle
Mathematical Biosciences 287, 42-53, 2017
Summary statistics
D Prangle
Handbook of Approximate Bayesian Computation, 125-152, 2018
Semi-automatic selection of summary statistics for ABC model choice
D Prangle, P Fearnhead, MP Cox, PJ Biggs, NP French
Statistical applications in genetics and molecular biology 13 (1), 67-82, 2014
Diagnostic tools for approximate Bayesian computation using the coverage property
D Prangle, MGB Blum, G Popovic, SA Sisson
Australian & New Zealand Journal of Statistics 56 (4), 309-329, 2014
Estimating age of mature adults from the degeneration of the sternal end of the clavicle
CG Falys, D Prangle
American journal of physical anthropology 156 (2), 203-214, 2015
Black-box variational inference for stochastic differential equations
T Ryder, A Golightly, AS McGough, D Prangle
International Conference on Machine Learning, 4423-4432, 2018
Lazy ABC
D Prangle
Statistics and Computing 26 (1-2), 171-185, 2016
abctools: an R package for tuning Approximate Bayesian Computation analyses
MA Nunes, D Prangle
The R Journal 7 (2), 189-205, 2015
A rare event approach to high-dimensional approximate Bayesian computation
D Prangle, RG Everitt, T Kypraios
Statistics and Computing 28 (4), 819-834, 2018
Recalibration: A post-processing method for approximate Bayesian computation
GS Rodrigues, D Prangle, SA Sisson
Computational Statistics & Data Analysis 126, 53-66, 2018
gk: An R Package for the g-and-k and generalised g-and-h Distributions
D Prangle
arXiv preprint arXiv:1706.06889, 2017
Ensemble MCMC: accelerating pseudo-marginal MCMC for state space models using the ensemble kalman filter
C Drovandi, RG Everitt, A Golightly, D Prangle
Bayesian Analysis, 2020
Taking Error Into Account When Fitting Models Using Approximate Bayesian Computation
E van der Vaart, D Prangle, RM Sibly
Ecological Applications, 2017
Distilling Importance Sampling for Likelihood Free Inference
D Prangle, C Viscardi
Journal of Computational and Graphical Statistics, 1-22, 2023
Measure transport with kernel Stein discrepancy
M Fisher, T Nolan, M Graham, D Prangle, C Oates
International Conference on Artificial Intelligence and Statistics, 1054-1062, 2021
Black-Box Inference for Non-Linear Latent Force Models
W Ward, T Ryder, D Prangle, M Alvarez
International Conference on Artificial Intelligence and Statistics, 3088-3098, 2020
Parametrised data sampling for fairness optimisation
V Zelaya, P Missier, D Prangle
KDD XAI, 2019
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